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Adversarial Attack

An Adversarial Attack is a technique to find a perturbation that changes the prediction of a machine learning model. The perturbation can be very small and imperceptible to human eyes.

Source: Recurrent Attention Model with Log-Polar Mapping is Robust against Adversarial Attacks

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 71–80 of 1808 papers

Title	Date	Tasks	Status	Hype
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers	Jan 1, 2024	Adversarial Attack	CodeCode Available	1
Transferable Structural Sparse Adversarial Attack Via Exact Group Sparsity Training	Jan 1, 2024	Adversarial Attackimage-classification	CodeCode Available	1
Towards Transferable Targeted 3D Adversarial Attack in the Physical World	Dec 15, 2023	Adversarial AttackNeRF	CodeCode Available	1
AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection	Dec 14, 2023	Adversarial AttackAttribute	CodeCode Available	1
An Extensive Study on Adversarial Attack against Pre-trained Models of Code	Nov 13, 2023	Adversarial Attack	CodeCode Available	1
AutoDAN: Interpretable Gradient-Based Adversarial Attacks on Large Language Models	Oct 23, 2023	Adversarial AttackBlocking	CodeCode Available	1
Targeted Attack Improves Protection against Unauthorized Diffusion Customization	Oct 7, 2023	Adversarial AttackImage Generation	CodeCode Available	1
Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks	Sep 29, 2023	Adversarial AttackFace Swapping	CodeCode Available	1
Structure Invariant Transformation for better Adversarial Transferability	Sep 26, 2023	Adversarial AttackDiversity	CodeCode Available	1
Semantic Adversarial Attacks via Diffusion Models	Sep 14, 2023	Adversarial Attack	CodeCode Available	1

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All datasets CIFAR-10 CIFAR-100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Xu et al.	Attack: PGD20	78.68	—	Unverified
2	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	78.13	—	Unverified
3	TRADES-ANCRA/ResNet18	Attack: AutoAttack	59.7	—	Unverified
4	AdvTraining [madry2018]	Attack: PGD20	48.44	—	Unverified
5	TRADES [zhang2019b]	Attack: PGD20	45.9	—	Unverified
6	XU-Net	Robust Accuracy	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	3-ensemble of multi-resolution self-ensembles	Attack: AutoAttack	51.28	—	Unverified
2	multi-resolution self-ensembles	Attack: AutoAttack	47.85	—	Unverified